Adult energy requirements predicted from doubly labeled water.

BACKGROUND: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative. METHODS: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases. RESULTS: Sixty-four different linear and nonlinear regression models were developed. The adjusted R2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.

A computational model to determine energy intake during weight loss.

BACKGROUND: Energy intake (EI) during weight loss is difficult and costly to measure accurately. OBJECTIVE: The objective was to develop and validate a computational energy balance differential equation model to determine individual EI during weight loss. DESIGN: An algorithm was developed to quantify EI during weight loss based on a validated one-dimensional model for weight change. By using data from a 24-wk calorie-restriction study, we tested the validity of the EI model against 2 criterion measures: 1) EI quantified through food provision from weeks 0-4 and 4-12 and 2) EI quantified through changes in body energy stores [measured with dual-energy X-ray absorptiometry (DXA)] and energy expenditure [measured with doubly labeled water (DLW)] from weeks 4-12 and 12-24. RESULTS: Compared with food provision, the mean (±SD) model errors were 41 ± 118 kcal/d and -22 ± 230 kcal/d from weeks 0-4 and 4-12, respectively. Compared with EI measured with DXA and DLW, the model errors were -71 ± 272 kcal/d and -48 ± 226 kcal/d from weeks 4-12 and 12-24, respectively. In every comparison, the mean error was never significantly different from zero (P values > 0.10). Furthermore, Bland and Altman analysis indicated that error variance did not differ significantly over amounts of EI (P values > 0.26). Almost all individual participants' values were within CI limits. CONCLUSION: The validity of the newly developed EI model was supported by experimental observations and can be used to determine an individual participant's EI during weight loss.